Data Analysis & Signature Generation Component Our goal is to generate cellular signatures of human neurons in response to perturbagens. Our studies will focus on human neurons, generated from induced pluripotent stem cells (iPSCs) (i-neurons) obtained from both healthy people and patients with neurodegenerative diseases. The cellular signature will be a composite picture of the molecular properties of a neuron that distinguish the state and determine the behavior of the cell. We will generate three classes of cellular signatures. The first will be static signatures based on quantitative molecular phenotyping involving OMIC analysis of the i-neurons. Analysis of the static signatures will highlight critical signaling pathways that distinguish a cellular response to a perturbagen. The second type of signature will be dynamic signatures generated with a novel high throughput, single cell longitudinal analysis system. Robotic Microscopy (RM). RM will be able to pinpoint critical times in the life of i-neurons as their physiology change in response to perturbagens. Analysis of dynamic signatures will guide selection of time points that will be investigated more in depth with methods that generate static signatures. In turn, elements of these static signatures will be perturbed genetically and analyzed by RM to elucidate the epistatic relationship of the components of a signature and to develop explicit multivariate predictive descriptions of cellular responses to perturbations. The third type of signature will emerge from an integration of the individual signatures using clustering methods and machine learning algorithms. The technology to analyze the data of the cellular signatures will be compatible with those produced at other sites in the LINCS network. A major innovation of our program is the implementation of novel data analysis platforms that will produce signatures that will have greater predictive value of a cell's biology than standard technologies. We will integrate Data Analysis and Data Generation, creating feedback loops to allow the cellular signatures that we generate to influence subsequent data generation. In turn, the use of machine learning algorithms in collaboration with Google will allow us to iteratively refine our signatures to make them more predictive in identifying cause and effect relationships from the cellular signatures.